Overview

Dataset statistics

Number of variables13
Number of observations29240
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 MiB
Average record size in memory104.0 B

Variable types

Numeric8
Text2
Categorical3

Alerts

F_AMOUNT_TRANSACTION is highly overall correlated with N_CURRENCY_CODE_TRANSACTIONHigh correlation
ID_TRX is highly overall correlated with N_TRANSMISSION_DATE_AND_TIMEHigh correlation
N_ACQ_INSTITUTION_COUNTRY_CODE is highly overall correlated with N_CURRENCY_CODE_TRANSACTIONHigh correlation
N_CURRENCY_CODE_TRANSACTION is highly overall correlated with F_AMOUNT_TRANSACTION and 1 other fieldsHigh correlation
N_TRANSMISSION_DATE_AND_TIME is highly overall correlated with ID_TRXHigh correlation
N_POINT_OF_SERV_COND_CODE is highly imbalanced (93.1%)Imbalance
F_AMOUNT_TRANSACTION is highly skewed (γ1 = 136.3851016)Skewed
F_DOLLAR_AMOUNT is highly skewed (γ1 = 20.57713058)Skewed
ID_TRX has unique valuesUnique

Reproduction

Analysis started2024-02-29 02:54:34.599641
Analysis finished2024-02-29 02:54:48.934229
Duration14.33 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

ID_TRX
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29240
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.072731 × 109
Minimum5.0655055 × 108
Maximum1.6627845 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:49.174029image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum5.0655055 × 108
5-th percentile5.683798 × 108
Q17.645199 × 108
median1.0543377 × 109
Q31.3782228 × 109
95-th percentile1.6050329 × 109
Maximum1.6627845 × 109
Range1.1562339 × 109
Interquartile range (IQR)6.1370286 × 108

Descriptive statistics

Standard deviation3.3767034 × 108
Coefficient of variation (CV)0.31477635
Kurtosis-1.2521703
Mean1.072731 × 109
Median Absolute Deviation (MAD)3.0670248 × 108
Skewness0.069225837
Sum3.1366654 × 1013
Variance1.1402126 × 1017
MonotonicityNot monotonic
2024-02-28T21:54:49.463848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
508924809 1
 
< 0.1%
1409285154 1
 
< 0.1%
1403738609 1
 
< 0.1%
1380962454 1
 
< 0.1%
1374590576 1
 
< 0.1%
1372321799 1
 
< 0.1%
1360071657 1
 
< 0.1%
1347126092 1
 
< 0.1%
1345864447 1
 
< 0.1%
1345467058 1
 
< 0.1%
Other values (29230) 29230
> 99.9%
ValueCountFrequency (%)
506550552 1
< 0.1%
506550741 1
< 0.1%
506562485 1
< 0.1%
506566788 1
< 0.1%
506592649 1
< 0.1%
506696925 1
< 0.1%
506722556 1
< 0.1%
506766633 1
< 0.1%
506772052 1
< 0.1%
506789927 1
< 0.1%
ValueCountFrequency (%)
1662784452 1
< 0.1%
1662774351 1
< 0.1%
1662774095 1
< 0.1%
1662751677 1
< 0.1%
1662704696 1
< 0.1%
1662638437 1
< 0.1%
1662610375 1
< 0.1%
1662608761 1
< 0.1%
1662604008 1
< 0.1%
1662603967 1
< 0.1%

S_PAN
Text

Distinct14387
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:49.733915image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters467840
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11017 ?
Unique (%)37.7%

Sample

1st row488245******4567
2nd row410443******1014
3rd row434527******5015
4th row410443******0018
5th row478787******2328
ValueCountFrequency (%)
423087******9005 82
 
0.3%
423087******5002 81
 
0.3%
423087******0001 79
 
0.3%
423087******7009 78
 
0.3%
423087******7004 76
 
0.3%
423087******4004 76
 
0.3%
423087******2005 76
 
0.3%
423087******6004 75
 
0.3%
423087******3002 75
 
0.3%
423087******2001 74
 
0.3%
Other values (14377) 28468
97.4%
2024-02-28T21:54:50.114864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 175440
37.5%
0 54129
 
11.6%
4 50699
 
10.8%
7 35381
 
7.6%
8 29193
 
6.2%
3 25196
 
5.4%
2 24922
 
5.3%
5 20764
 
4.4%
1 20138
 
4.3%
9 19040
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 292400
62.5%
Other Punctuation 175440
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54129
18.5%
4 50699
17.3%
7 35381
12.1%
8 29193
10.0%
3 25196
8.6%
2 24922
8.5%
5 20764
 
7.1%
1 20138
 
6.9%
9 19040
 
6.5%
6 12938
 
4.4%
Other Punctuation
ValueCountFrequency (%)
* 175440
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 467840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 175440
37.5%
0 54129
 
11.6%
4 50699
 
10.8%
7 35381
 
7.6%
8 29193
 
6.2%
3 25196
 
5.4%
2 24922
 
5.3%
5 20764
 
4.4%
1 20138
 
4.3%
9 19040
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 467840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 175440
37.5%
0 54129
 
11.6%
4 50699
 
10.8%
7 35381
 
7.6%
8 29193
 
6.2%
3 25196
 
5.4%
2 24922
 
5.3%
5 20764
 
4.4%
1 20138
 
4.3%
9 19040
 
4.1%

S_ENCRYPTED_PAN
Real number (ℝ)

Distinct22309
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0001039 × 1015
Minimum1 × 1015
Maximum1.0002567 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:50.302745image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1 × 1015
5-th percentile1.0000008 × 1015
Q11.0000077 × 1015
median1.0000666 × 1015
Q31.0002022 × 1015
95-th percentile1.0002413 × 1015
Maximum1.0002567 × 1015
Range2.5674814 × 1011
Interquartile range (IQR)1.9447214 × 1011

Descriptive statistics

Standard deviation9.1528062 × 1010
Coefficient of variation (CV)9.1518553 × 10-5
Kurtosis-1.6155555
Mean1.0001039 × 1015
Median Absolute Deviation (MAD)6.3440494 × 1010
Skewness0.24884125
Sum-7.6504498 × 1018
Variance8.3773862 × 1021
MonotonicityNot monotonic
2024-02-28T21:54:50.490595image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.000007246 × 101532
 
0.1%
1.000003311 × 101532
 
0.1%
1.000209985 × 101531
 
0.1%
1.000002614 × 101531
 
0.1%
1.000004212 × 101530
 
0.1%
1.000000295 × 101529
 
0.1%
1.000119843 × 101528
 
0.1%
1.000002075 × 101528
 
0.1%
1.000017191 × 101528
 
0.1%
1.000002493 × 101527
 
0.1%
Other values (22299) 28944
99.0%
ValueCountFrequency (%)
1.000000001 × 10151
< 0.1%
1.000000001 × 10152
< 0.1%
1.000000002 × 10151
< 0.1%
1.000000004 × 10151
< 0.1%
1.000000004 × 10151
< 0.1%
1.000000004 × 10151
< 0.1%
1.000000005 × 10151
< 0.1%
1.000000005 × 10151
< 0.1%
1.000000005 × 10151
< 0.1%
1.000000008 × 10151
< 0.1%
ValueCountFrequency (%)
1.000256749 × 10151
< 0.1%
1.000256529 × 10151
< 0.1%
1.000256476 × 10151
< 0.1%
1.00025647 × 10151
< 0.1%
1.000256452 × 10151
< 0.1%
1.000256441 × 10151
< 0.1%
1.000256379 × 10151
< 0.1%
1.00025627 × 10151
< 0.1%
1.000256253 × 10151
< 0.1%
1.000256151 × 10151
< 0.1%

F_AMOUNT_TRANSACTION
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct9662
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17227.87
Minimum-70000
Maximum2.0779638 × 108
Zeros0
Zeros (%)0.0%
Negative80
Negative (%)0.3%
Memory size228.6 KiB
2024-02-28T21:54:50.679047image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum-70000
5-th percentile1
Q110.99
median126.34
Q32300
95-th percentile16211.493
Maximum2.0779638 × 108
Range2.0786638 × 108
Interquartile range (IQR)2289.01

Descriptive statistics

Standard deviation1364286.5
Coefficient of variation (CV)79.190667
Kurtosis19596.176
Mean17227.87
Median Absolute Deviation (MAD)125.34
Skewness136.3851
Sum5.0374293 × 108
Variance1.8612778 × 1012
MonotonicityNot monotonic
2024-02-28T21:54:50.845409image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1162
 
4.0%
5.99 811
 
2.8%
100 565
 
1.9%
9.99 493
 
1.7%
630 463
 
1.6%
4.99 432
 
1.5%
3000 414
 
1.4%
0.99 389
 
1.3%
50 362
 
1.2%
25 322
 
1.1%
Other values (9652) 23827
81.5%
ValueCountFrequency (%)
-70000 1
< 0.1%
-67700 1
< 0.1%
-63500 1
< 0.1%
-50000 1
< 0.1%
-25000 1
< 0.1%
-20000 2
< 0.1%
-17350 1
< 0.1%
-15151 1
< 0.1%
-15000 1
< 0.1%
-14000 1
< 0.1%
ValueCountFrequency (%)
207796380 1
< 0.1%
104904965.7 1
< 0.1%
7772996 1
< 0.1%
7000000 1
< 0.1%
6621043 1
< 0.1%
3981922 1
< 0.1%
3691900 1
< 0.1%
3608218 1
< 0.1%
2188794 1
< 0.1%
1927181 1
< 0.1%

N_CURRENCY_CODE_TRANSACTION
Real number (ℝ)

HIGH CORRELATION 

Distinct57
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean501.958
Minimum32
Maximum986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:51.041872image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile188
Q1188
median340
Q3840
95-th percentile840
Maximum986
Range954
Interquartile range (IQR)652

Descriptive statistics

Standard deviation306.69372
Coefficient of variation (CV)0.61099477
Kurtosis-1.8631959
Mean501.958
Median Absolute Deviation (MAD)152
Skewness0.15081507
Sum14677252
Variance94061.036
MonotonicityNot monotonic
2024-02-28T21:54:51.214512image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
840 12328
42.2%
188 10916
37.3%
340 4553
 
15.6%
978 213
 
0.7%
484 186
 
0.6%
124 179
 
0.6%
214 125
 
0.4%
170 101
 
0.3%
826 76
 
0.3%
784 65
 
0.2%
Other values (47) 498
 
1.7%
ValueCountFrequency (%)
32 11
 
< 0.1%
36 19
 
0.1%
50 1
 
< 0.1%
60 1
 
< 0.1%
68 4
 
< 0.1%
84 49
 
0.2%
124 179
0.6%
136 4
 
< 0.1%
152 9
 
< 0.1%
156 5
 
< 0.1%
ValueCountFrequency (%)
986 23
 
0.1%
985 4
 
< 0.1%
981 1
 
< 0.1%
978 213
 
0.7%
949 8
 
< 0.1%
946 3
 
< 0.1%
928 2
 
< 0.1%
858 3
 
< 0.1%
840 12328
42.2%
834 1
 
< 0.1%

F_DOLLAR_AMOUNT
Real number (ℝ)

SKEWED 

Distinct12786
Distinct (%)43.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48.387343
Minimum0.007
Maximum11678
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:51.386342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum0.007
5-th percentile0.998
Q12.84
median7.0535
Q320.96
95-th percentile159.1702
Maximum11678
Range11677.993
Interquartile range (IQR)18.12

Descriptive statistics

Standard deviation272.21061
Coefficient of variation (CV)5.6256574
Kurtosis591.48166
Mean48.387343
Median Absolute Deviation (MAD)5.916
Skewness20.577131
Sum1414845.9
Variance74098.618
MonotonicityNot monotonic
2024-02-28T21:54:51.573808image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 457
 
1.6%
5.99 257
 
0.9%
9.99 243
 
0.8%
1.008 201
 
0.7%
4.99 170
 
0.6%
0.998 152
 
0.5%
1.009 142
 
0.5%
0.99 137
 
0.5%
0.042 121
 
0.4%
19.99 116
 
0.4%
Other values (12776) 27244
93.2%
ValueCountFrequency (%)
0.007 1
 
< 0.1%
0.01 24
 
0.1%
0.014 1
 
< 0.1%
0.016 3
 
< 0.1%
0.017 1
 
< 0.1%
0.018 1
 
< 0.1%
0.02 3
 
< 0.1%
0.03 4
 
< 0.1%
0.04 6
 
< 0.1%
0.041 76
0.3%
ValueCountFrequency (%)
11678 1
< 0.1%
10650.01 1
< 0.1%
10000 2
< 0.1%
9419.86 1
< 0.1%
8595.41 1
< 0.1%
7845.66 1
< 0.1%
7663.89 1
< 0.1%
7020.46 1
< 0.1%
6850.48 1
< 0.1%
6766.09 1
< 0.1%

N_TRANSMISSION_DATE_AND_TIME
Real number (ℝ)

HIGH CORRELATION 

Distinct29224
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0208623 × 1013
Minimum2.0201001 × 1013
Maximum2.021113 × 1013
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:51.746692image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum2.0201001 × 1013
5-th percentile2.0201026 × 1013
Q12.0210117 × 1013
median2.0210506 × 1013
Q32.0210818 × 1013
95-th percentile2.0211109 × 1013
Maximum2.021113 × 1013
Range1.0129209 × 1010
Interquartile range (IQR)7.0102528 × 108

Descriptive statistics

Standard deviation3.8853951 × 109
Coefficient of variation (CV)0.00019226421
Kurtosis-0.0081577972
Mean2.0208623 × 1013
Median Absolute Deviation (MAD)3.2405363 × 108
Skewness-1.4005462
Sum5.9090015 × 1017
Variance1.5096295 × 1019
MonotonicityNot monotonic
2024-02-28T21:54:52.101163image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.021011608 × 10132
 
< 0.1%
2.021092423 × 10132
 
< 0.1%
2.020120214 × 10132
 
< 0.1%
2.021042615 × 10132
 
< 0.1%
2.021052924 × 10132
 
< 0.1%
2.021061011 × 10132
 
< 0.1%
2.021081523 × 10132
 
< 0.1%
2.02105311 × 10132
 
< 0.1%
2.021011414 × 10132
 
< 0.1%
2.021102612 × 10132
 
< 0.1%
Other values (29214) 29220
99.9%
ValueCountFrequency (%)
2.020100101 × 10131
< 0.1%
2.020100101 × 10131
< 0.1%
2.020100102 × 10131
< 0.1%
2.020100102 × 10131
< 0.1%
2.020100102 × 10131
< 0.1%
2.020100103 × 10131
< 0.1%
2.020100104 × 10131
< 0.1%
2.020100105 × 10131
< 0.1%
2.020100105 × 10131
< 0.1%
2.020100105 × 10131
< 0.1%
ValueCountFrequency (%)
2.021113022 × 10131
< 0.1%
2.021113021 × 10131
< 0.1%
2.021113021 × 10131
< 0.1%
2.021113021 × 10131
< 0.1%
2.02111302 × 10131
< 0.1%
2.021113019 × 10131
< 0.1%
2.021113019 × 10131
< 0.1%
2.021113019 × 10131
< 0.1%
2.021113019 × 10131
< 0.1%
2.021113019 × 10131
< 0.1%

N_MERCHANT_TYPE
Real number (ℝ)

Distinct221
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5656.3696
Minimum1771
Maximum9406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:52.332371image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum1771
5-th percentile4121
Q14814
median5814
Q35818
95-th percentile7832
Maximum9406
Range7635
Interquartile range (IQR)1004

Descriptive statistics

Standard deviation1137.4609
Coefficient of variation (CV)0.20109382
Kurtosis1.3072733
Mean5656.3696
Median Absolute Deviation (MAD)193
Skewness0.82384581
Sum1.6539225 × 108
Variance1293817.4
MonotonicityNot monotonic
2024-02-28T21:54:52.504605image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4121 4769
16.3%
5816 3169
 
10.8%
5812 1745
 
6.0%
5818 1553
 
5.3%
5815 1327
 
4.5%
5734 1231
 
4.2%
4812 1228
 
4.2%
5817 1125
 
3.8%
5942 905
 
3.1%
5814 856
 
2.9%
Other values (211) 11332
38.8%
ValueCountFrequency (%)
1771 4
 
< 0.1%
2741 5
 
< 0.1%
2842 1
 
< 0.1%
3000 15
0.1%
3001 4
 
< 0.1%
3007 1
 
< 0.1%
3014 1
 
< 0.1%
3039 6
 
< 0.1%
3052 2
 
< 0.1%
3058 5
 
< 0.1%
ValueCountFrequency (%)
9406 179
0.6%
9399 236
0.8%
9311 88
 
0.3%
9222 1
 
< 0.1%
8999 157
0.5%
8931 2
 
< 0.1%
8911 5
 
< 0.1%
8734 1
 
< 0.1%
8699 31
 
0.1%
8675 1
 
< 0.1%

N_ACQ_INSTITUTION_COUNTRY_CODE
Real number (ℝ)

HIGH CORRELATION 

Distinct70
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean611.35595
Minimum32
Maximum862
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:52.728708image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile188
Q1372
median702
Q3840
95-th percentile840
Maximum862
Range830
Interquartile range (IQR)468

Descriptive statistics

Standard deviation232.63892
Coefficient of variation (CV)0.38052942
Kurtosis-1.1807545
Mean611.35595
Median Absolute Deviation (MAD)138
Skewness-0.46938145
Sum17876048
Variance54120.868
MonotonicityNot monotonic
2024-02-28T21:54:53.007550image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
840 8314
28.4%
528 4837
16.5%
826 4659
15.9%
372 2779
 
9.5%
340 2541
 
8.7%
188 1838
 
6.3%
702 1602
 
5.5%
591 925
 
3.2%
344 194
 
0.7%
124 181
 
0.6%
Other values (60) 1370
 
4.7%
ValueCountFrequency (%)
32 3
 
< 0.1%
36 29
 
0.1%
40 1
 
< 0.1%
56 110
0.4%
60 1
 
< 0.1%
68 4
 
< 0.1%
76 20
 
0.1%
84 1
 
< 0.1%
100 3
 
< 0.1%
124 181
0.6%
ValueCountFrequency (%)
862 2
 
< 0.1%
858 1
 
< 0.1%
840 8314
28.4%
826 4659
15.9%
818 24
 
0.1%
804 1
 
< 0.1%
792 6
 
< 0.1%
784 60
 
0.2%
764 1
 
< 0.1%
756 5
 
< 0.1%

N_ENTRY_MODE
Categorical

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.6 KiB
10
14666 
100
8166 
102
3704 
12
2688 
11
 
16

Length

Max length3
Median length2
Mean length2.4059508
Min length2

Characters and Unicode

Total characters70350
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row102
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10 14666
50.2%
100 8166
27.9%
102 3704
 
12.7%
12 2688
 
9.2%
11 16
 
0.1%

Length

2024-02-28T21:54:53.209797image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T21:54:53.397302image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
10 14666
50.2%
100 8166
27.9%
102 3704
 
12.7%
12 2688
 
9.2%
11 16
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 34702
49.3%
1 29256
41.6%
2 6392
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70350
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34702
49.3%
1 29256
41.6%
2 6392
 
9.1%

Most occurring scripts

ValueCountFrequency (%)
Common 70350
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34702
49.3%
1 29256
41.6%
2 6392
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34702
49.3%
1 29256
41.6%
2 6392
 
9.1%

N_POINT_OF_SERV_COND_CODE
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.6 KiB
59
28999 
1
 
241

Length

Max length2
Median length2
Mean length1.9917579
Min length1

Characters and Unicode

Total characters58239
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row59
2nd row59
3rd row59
4th row59
5th row59

Common Values

ValueCountFrequency (%)
59 28999
99.2%
1 241
 
0.8%

Length

2024-02-28T21:54:53.603901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T21:54:53.781565image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
59 28999
99.2%
1 241
 
0.8%

Most occurring characters

ValueCountFrequency (%)
5 28999
49.8%
9 28999
49.8%
1 241
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58239
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 28999
49.8%
9 28999
49.8%
1 241
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 58239
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 28999
49.8%
9 28999
49.8%
1 241
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58239
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 28999
49.8%
9 28999
49.8%
1 241
 
0.4%
Distinct4852
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Memory size228.6 KiB
2024-02-28T21:54:54.135524image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Length

Max length22
Median length19
Mean length16.078967
Min length2

Characters and Unicode

Total characters470149
Distinct characters79
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3740 ?
Unique (%)12.8%

Sample

1st rowGOOGLE *GSUITE_inversi
2nd rowUBR* PENDING.UBER.COM
3rd rowGoogle LLC GSUITE_iyta
4th rowRAPPI
5th rowFLEXI SHOES
ValueCountFrequency (%)
google 4504
 
6.8%
paypal 2433
 
3.7%
garena 2254
 
3.4%
costa 1848
 
2.8%
rica 1826
 
2.8%
uber 1821
 
2.7%
apple.com/bill 1556
 
2.3%
ubr 1534
 
2.3%
pending.uber.com 1533
 
2.3%
boacompra 1453
 
2.2%
Other values (5604) 45504
68.7%
2024-02-28T21:54:54.885691image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
39298
 
8.4%
A 34898
 
7.4%
E 32115
 
6.8%
O 31367
 
6.7%
R 25030
 
5.3%
P 22822
 
4.9%
L 21166
 
4.5%
C 19752
 
4.2%
G 17798
 
3.8%
I 16645
 
3.5%
Other values (69) 209258
44.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 331067
70.4%
Lowercase Letter 66614
 
14.2%
Space Separator 39298
 
8.4%
Other Punctuation 21606
 
4.6%
Decimal Number 10787
 
2.3%
Dash Punctuation 649
 
0.1%
Connector Punctuation 93
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Math Symbol 9
 
< 0.1%
Close Punctuation 9
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 34898
 
10.5%
E 32115
 
9.7%
O 31367
 
9.5%
R 25030
 
7.6%
P 22822
 
6.9%
L 21166
 
6.4%
C 19752
 
6.0%
G 17798
 
5.4%
I 16645
 
5.0%
M 13904
 
4.2%
Other values (16) 95570
28.9%
Lowercase Letter
ValueCountFrequency (%)
i 10548
15.8%
o 5761
 
8.6%
e 5140
 
7.7%
a 4761
 
7.1%
t 4051
 
6.1%
n 3914
 
5.9%
d 3119
 
4.7%
p 3114
 
4.7%
c 2523
 
3.8%
g 2506
 
3.8%
Other values (16) 21177
31.8%
Other Punctuation
ValueCountFrequency (%)
. 10073
46.6%
* 9579
44.3%
/ 1826
 
8.5%
& 66
 
0.3%
, 38
 
0.2%
@ 11
 
0.1%
# 9
 
< 0.1%
: 2
 
< 0.1%
? 1
 
< 0.1%
! 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
2 1741
16.1%
1 1522
14.1%
3 1151
10.7%
0 1064
9.9%
4 959
8.9%
7 955
8.9%
5 924
8.6%
6 875
8.1%
8 817
7.6%
9 779
7.2%
Space Separator
ValueCountFrequency (%)
39298
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 649
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 93
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Math Symbol
ValueCountFrequency (%)
+ 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 397681
84.6%
Common 72468
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 34898
 
8.8%
E 32115
 
8.1%
O 31367
 
7.9%
R 25030
 
6.3%
P 22822
 
5.7%
L 21166
 
5.3%
C 19752
 
5.0%
G 17798
 
4.5%
I 16645
 
4.2%
M 13904
 
3.5%
Other values (42) 162184
40.8%
Common
ValueCountFrequency (%)
39298
54.2%
. 10073
 
13.9%
* 9579
 
13.2%
/ 1826
 
2.5%
2 1741
 
2.4%
1 1522
 
2.1%
3 1151
 
1.6%
0 1064
 
1.5%
4 959
 
1.3%
7 955
 
1.3%
Other values (17) 4300
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 470149
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
39298
 
8.4%
A 34898
 
7.4%
E 32115
 
6.8%
O 31367
 
6.7%
R 25030
 
5.3%
P 22822
 
4.9%
L 21166
 
4.5%
C 19752
 
4.2%
G 17798
 
3.8%
I 16645
 
3.5%
Other values (69) 209258
44.5%

FRAUDE
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size228.6 KiB
0
23392 
1
5848 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters29240
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 23392
80.0%
1 5848
 
20.0%

Length

2024-02-28T21:54:55.071538image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-28T21:54:55.268152image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
ValueCountFrequency (%)
0 23392
80.0%
1 5848
 
20.0%

Most occurring characters

ValueCountFrequency (%)
0 23392
80.0%
1 5848
 
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29240
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 23392
80.0%
1 5848
 
20.0%

Most occurring scripts

ValueCountFrequency (%)
Common 29240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 23392
80.0%
1 5848
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 23392
80.0%
1 5848
 
20.0%

Interactions

2024-02-28T21:54:46.140387image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:36.735901image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:38.103709image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:39.670433image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:40.919721image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:42.138620image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:43.401945image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:44.805211image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:46.448570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:36.903045image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:38.388090image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:39.839253image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:41.087311image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:42.287131image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:43.578174image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:44.991251image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:46.956741image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:37.056355image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:38.610583image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:39.985566image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:41.242460image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:42.451071image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:43.735792image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:45.172810image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:47.136848image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:37.198295image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:38.806960image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:40.120568image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:41.402863image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:42.587504image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:43.870209image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:45.340067image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:47.305207image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:37.373756image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:39.024743image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:40.278557image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:41.534778image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:42.745647image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:44.077832image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:45.471523image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:47.562414image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:37.563760image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:39.221514image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:40.436413image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:41.669133image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:42.941825image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:44.303977image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:45.638658image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:47.764151image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:37.736909image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:39.370492image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:40.586784image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:41.814978image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:43.087342image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:44.468332image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:45.797316image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:48.023537image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:37.920651image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:39.544932image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:40.770906image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:42.007375image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:43.268520image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:44.623777image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
2024-02-28T21:54:45.938570image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/

Correlations

2024-02-28T21:54:55.386127image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
FRAUDEF_AMOUNT_TRANSACTIONF_DOLLAR_AMOUNTID_TRXN_ACQ_INSTITUTION_COUNTRY_CODEN_CURRENCY_CODE_TRANSACTIONN_ENTRY_MODEN_MERCHANT_TYPEN_POINT_OF_SERV_COND_CODEN_TRANSMISSION_DATE_AND_TIMES_ENCRYPTED_PAN
FRAUDE1.0000.1480.236-0.0050.097-0.0150.1380.0730.000-0.005-0.129
F_AMOUNT_TRANSACTION0.1481.0000.336-0.024-0.471-0.8070.000-0.1680.000-0.024-0.167
F_DOLLAR_AMOUNT0.2360.3361.000-0.0150.0020.1720.0410.1630.029-0.015-0.146
ID_TRX-0.005-0.024-0.0151.000-0.0610.0160.0660.0140.0241.0000.268
N_ACQ_INSTITUTION_COUNTRY_CODE0.097-0.4710.002-0.0611.0000.5300.3850.2560.128-0.0610.009
N_CURRENCY_CODE_TRANSACTION-0.015-0.8070.1720.0160.5301.0000.2110.2450.0370.0160.068
N_ENTRY_MODE0.1380.0000.0410.0660.3850.2111.000-0.2330.125-0.0140.052
N_MERCHANT_TYPE0.073-0.1680.1630.0140.2560.245-0.2331.0000.2260.0140.026
N_POINT_OF_SERV_COND_CODE0.0000.0000.0290.0240.1280.0370.1250.2261.000-0.0240.036
N_TRANSMISSION_DATE_AND_TIME-0.005-0.024-0.0151.000-0.0610.016-0.0140.014-0.0241.0000.268
S_ENCRYPTED_PAN-0.129-0.167-0.1460.2680.0090.0680.0520.0260.0360.2681.000

Missing values

2024-02-28T21:54:48.273900image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-28T21:54:48.648864image/svg+xmlMatplotlib v3.8.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

ID_TRXS_PANS_ENCRYPTED_PANF_AMOUNT_TRANSACTIONN_CURRENCY_CODE_TRANSACTIONF_DOLLAR_AMOUNTN_TRANSMISSION_DATE_AND_TIMEN_MERCHANT_TYPEN_ACQ_INSTITUTION_COUNTRY_CODEN_ENTRY_MODEN_POINT_OF_SERV_COND_CODES_MERCHANT_LEGAL_NAMEFRAUDE
0508924809488245******456710000652758145675.408405.3882020100119073273728401059GOOGLE *GSUITE_inversi0
1509028097410443******101410001262927710143350.001885.57020201001194719412137210259UBR* PENDING.UBER.COM0
2511351088434527******5015100000743374501510.8084010.8002020100209553448168401059Google LLC GSUITE_iyta0
3515833505410443******001810001265305700184960.801888.2802020100410141948141881059RAPPI0
4519145453478787******2328100002038254232840250.0018867.1772020100517324156611881059FLEXI SHOES0
5523991261434527******800710000078563380075.998405.99020201007192629581552810059Spotify P11A48F8470
6530513150476528******7304100000047783730429.2484029.2402020101013331459428401059Amazon Payments0
7542249902424905******290010000566028329009040.0018815.08320201015182823581237210259UBR* PENDING.UBER.COM0
8542299586424905******943910000447547194391405.001882.34420201015185509412170210059DidiChuxing0
9546057827423087******600210001988822260021.098401.0992020101709550658168401259GOOGLE*GARENA0
ID_TRXS_PANS_ENCRYPTED_PANF_AMOUNT_TRANSACTIONN_CURRENCY_CODE_TRANSACTIONF_DOLLAR_AMOUNTN_TRANSMISSION_DATE_AND_TIMEN_MERCHANT_TYPEN_ACQ_INSTITUTION_COUNTRY_CODEN_ENTRY_MODEN_POINT_OF_SERV_COND_CODES_MERCHANT_LEGAL_NAMEFRAUDE
292301618312961411757******3034100005141657303449.9984049.99020211114042804581584010059APPLE.COM/BILL1
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